Identification pattern systems, such as ten print or fingerprint and slap print identification systems, play a critical role in modern society in both criminal and civil applications. For example, criminal identification in public safety sectors is an integral part of any present day investigation. Similarly in civil applications such as credit card or personal identity fraud, print identification has become an essential part of the security process.
An automatic fingerprint identification operation normally consists of two stages. The first is the registration stage and the second is the identification stage. In the registration stage, the register's prints and personal information are enrolled and features, such as minutiae, are extracted. The prints, personal information and the extracted features are then used to form a file record that is saved into a database for subsequent print identification. Present day automatic fingerprint identification systems may contain several hundred thousand to several million of such file records.
In the identification stage, print features from an individual, or latent print, and personal information are extracted to form what is typically referred to as a search record. The search record is then compared with the enrolled file records in the database of the fingerprint matching system. In a typical search scenario, a search record may be compared against millions of file records that are stored in the database and a list of matched scores are generated after the matching process. Candidate records are sorted according to matched prints scores. A matched score is a measurement of the similarity of the print features of the identified search and file records. The higher the score is, the more similar the file and search records are determined to be. Thus, a top candidate is the one that has the closest match.
In Automated Fingerprint Identification Systems (AFIS), the fingerprint data is collected in the form of fourteen inked impressions on a traditional tenprint card. This is comprised of the rolled impressions of the ten fingers as well as four slap impressions: the left slap (four fingers of the left hand), the right slap (the four fingers of the right hand) and the thumb slaps (the left and right thumbs). Nowadays these fourteen images are obtained using a Live Scan workstation. Validating the accuracy of such captured prints is generally accomplished by the matching of each rolled or flat finger print against the corresponding finger of a slap print, preferably taken from both hands of the owner, referred to as the rolled to slap comparison or RTS. The validation of this comparison critically depends on the accuracy of the captured data from top regions of the fingers obtained in the slap prints.
To insure proper segmentation, each segmented image must be visually inspected and corrected. The amount of time and energy to manually review an enrolled card in a large AFIS system is a drain on resources that are becoming more stretched as the demand for fast and accurate identification services are required.
In the prior art, the goal of print segmentation is to identify the foreground and background of an image using a single print segmentation algorithm that is limited in its use to single print images and that can not be directly applied to slap print images. Since the enrolled slap prints are often rotated or not oriented properly on the media in which they are captured, a simple top edge center point or the centroid of a component, like the segments detected in prior art methods, do not result in the accurate assignment of the proper finger print number to the detected component. For example, if a right hand slap impression is rotated more than 45 degrees toward the left side of the print, the top edge of the middle fingerprint (finger #3) component may be closer to left side edge of the print than that of the index fingerprint (#2) component. If a right hand slap impression is rotated more than 45 degrees toward to the right side of the print, the top edge of the middle finger (finger #3), and the ring finger (finger #4) may be closer to right side edge of the print than that of the little finger (finger #5). If one uses a rule that finger #5 is most closest to the right side of the print and finger #2 is the most closest to the left side of the print, then the finger numbers will be assigned wrongly when the fingerprint slap impression are rotated as described above.
Thus, it would be desirable to have a system and method that improves the accuracy of data captured from the segments, preferably the top regions, of each finger of the slap prints to compare with other print data in the AFIS system.